In the case of the PPI dataset, we train the GAT [1]} student model on the visible graph to make predictions on completely invisible graphs. We refer to the experiment settings of [2]}, where the learning rate, weight decay, optimizer and epochs are 0.005, 0, Adam and 300, respectively. We adopt the RBF kernel function for distance calculation in LSP module, and \(\sigma \)  is 100.
While on other four datasets, we apply GCN [3]} and three variants of SAGE [4]} as student models to predict the unknown nodes on the visible graph.
We adopt one set of experiment settings from [5]}, where the learning rate, weight decay, optimizer and epochs are 0.001, 0.0005, Adam and 200, respectively, and the Euclidean kernel function is adopted for all models. All our experiments are performed on an NVIDIA 2080Ti GPU.
